



'''simple docstring'''
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)






@flax.struct.dataclass
class    __lowerCamelCase							( a_   ):







	'''simple docstring'''
	snake_case__		:   jnp.ndarray
	snake_case__		:   jnp.ndarray






class    __lowerCamelCase							( nn.Module   ):







	'''simple docstring'''
	snake_case__		:   int
	snake_case__		:   Tuple[int]       =				(16, 32, 96, 256)
	snake_case__		:   jnp.dtype       =				jnp.floataa






	def  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    Any						    =							nn.Conv(
						    self.block_out_channels[0]      ,     kernel_size=(3, 3)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )

						__SCREAMING_SNAKE_CASE				:    List[str]						    =							[]
						for i in range(len(self.block_out_channels					) - 1					):
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							self.block_out_channels[i]
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							self.block_out_channels[i + 1]
											__SCREAMING_SNAKE_CASE				:    Dict						    =							nn.Conv(
											    _lowercase      ,     kernel_size=(3, 3)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )
											blocks.append(_lowercase					)
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							nn.Conv(
											    _lowercase      ,     kernel_size=(3, 3)      ,     strides=(2, 2)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )
											blocks.append(_lowercase					)
						__SCREAMING_SNAKE_CASE				:    str						    =							blocks

						__SCREAMING_SNAKE_CASE				:    Any						    =							nn.Conv(
						    self.conditioning_embedding_channels      ,     kernel_size=(3, 3)      ,     padding=((1, 1), (1, 1))      ,     kernel_init=nn.initializers.zeros_init()      ,     bias_init=nn.initializers.zeros_init()      ,     dtype=self.dtype      ,     )






	def __call__(      self      ,     a__					):
						__SCREAMING_SNAKE_CASE				:    str						    =							self.conv_in(_lowercase					)
						__SCREAMING_SNAKE_CASE				:    Any						    =							nn.silu(_lowercase					)

						for block in self.blocks:
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							block(_lowercase					)
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							nn.silu(_lowercase					)

						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							self.conv_out(_lowercase					)

						return embedding






@flax_register_to_config
class    __lowerCamelCase							( nn.Module							,							a_							,							a_   ):







	'''simple docstring'''
	snake_case__		:   int       =				32
	snake_case__		:   int       =				4
	snake_case__		:   Tuple[str]       =				(
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "CrossAttnDownBlock2D",
	    "DownBlock2D",
	)
	snake_case__		:   Union[bool, Tuple[bool]]       =				False
	snake_case__		:   Tuple[int]       =				(320, 640, 1280, 1280)
	snake_case__		:   int       =				2
	snake_case__		:   Union[int, Tuple[int]]       =				8
	snake_case__		:   Optional[Union[int, Tuple[int]]]       =				None
	snake_case__		:   int       =				1280
	snake_case__		:   float       =				0.0
	snake_case__		:   bool       =				False
	snake_case__		:   jnp.dtype       =				jnp.floataa
	snake_case__		:   bool       =				True
	snake_case__		:   int       =				0
	snake_case__		:   str       =				"rgb"
	snake_case__		:   Tuple[int]       =				(16, 32, 96, 256)






	def  a_					(      self      ,     a__					):
						# init input tensors
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							(1, self.in_channels, self.sample_size, self.sample_size)
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							jnp.zeros(_lowercase      ,     dtype=jnp.floataa					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							jnp.ones((1,)      ,     dtype=jnp.intaa					)
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							jnp.zeros((1, 1, self.cross_attention_dim)      ,     dtype=jnp.floataa					)
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							(1, 3, self.sample_size * 8, self.sample_size * 8)
						__SCREAMING_SNAKE_CASE				:    int						    =							jnp.zeros(_lowercase      ,     dtype=jnp.floataa					)

						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							jax.random.split(_lowercase					)
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							{"""params""": params_rng, """dropout""": dropout_rng}

						return self.init(_lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase      ,     _lowercase					)["params"]






	def  a_					(      self					):
						__SCREAMING_SNAKE_CASE				:    Dict						    =							self.block_out_channels
						__SCREAMING_SNAKE_CASE				:    Any						    =							block_out_channels[0] * 4

						# If `num_attention_heads` is not defined (which is the case for most models)
						# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
						# The reason for this behavior is to correct for incorrectly named variables that were introduced
						# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
						# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
						# which is why we correct for the naming here.
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							self.num_attention_heads or self.attention_head_dim

						# input
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							nn.Conv(
						    block_out_channels[0]      ,     kernel_size=(3, 3)      ,     strides=(1, 1)      ,     padding=((1, 1), (1, 1))      ,     dtype=self.dtype      ,     )

						# time
						__SCREAMING_SNAKE_CASE				:    int						    =							FlaxTimesteps(
						    block_out_channels[0]      ,     flip_sin_to_cos=self.flip_sin_to_cos      ,     freq_shift=self.config.freq_shift					)
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							FlaxTimestepEmbedding(_lowercase      ,     dtype=self.dtype					)

						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							FlaxControlNetConditioningEmbedding(
						    conditioning_embedding_channels=block_out_channels[0]      ,     block_out_channels=self.conditioning_embedding_out_channels      ,     )

						__SCREAMING_SNAKE_CASE				:    Any						    =							self.only_cross_attention
						if isinstance(_lowercase      ,     _lowercase					):
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							(only_cross_attention,) * len(self.down_block_types					)

						if isinstance(_lowercase      ,     _lowercase					):
											__SCREAMING_SNAKE_CASE				:    Any						    =							(num_attention_heads,) * len(self.down_block_types					)

						# down
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							[]
						__SCREAMING_SNAKE_CASE				:    int						    =							[]

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							block_out_channels[0]

						__SCREAMING_SNAKE_CASE				:    str						    =							nn.Conv(
						    _lowercase      ,     kernel_size=(1, 1)      ,     padding="VALID"      ,     kernel_init=nn.initializers.zeros_init()      ,     bias_init=nn.initializers.zeros_init()      ,     dtype=self.dtype      ,     )
						controlnet_down_blocks.append(_lowercase					)

						for i, down_block_type in enumerate(self.down_block_types					):
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							output_channel
											__SCREAMING_SNAKE_CASE				:    str						    =							block_out_channels[i]
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							i == len(_lowercase					) - 1

											if down_block_type == "CrossAttnDownBlock2D":
																__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							FlaxCrossAttnDownBlockaD(
																    in_channels=_lowercase      ,     out_channels=_lowercase      ,     dropout=self.dropout      ,     num_layers=self.layers_per_block      ,     num_attention_heads=num_attention_heads[i]      ,     add_downsample=not is_final_block      ,     use_linear_projection=self.use_linear_projection      ,     only_cross_attention=only_cross_attention[i]      ,     dtype=self.dtype      ,     )
											else:
																__SCREAMING_SNAKE_CASE				:    Any						    =							FlaxDownBlockaD(
																    in_channels=_lowercase      ,     out_channels=_lowercase      ,     dropout=self.dropout      ,     num_layers=self.layers_per_block      ,     add_downsample=not is_final_block      ,     dtype=self.dtype      ,     )

											down_blocks.append(_lowercase					)

											for _ in range(self.layers_per_block					):
																__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							nn.Conv(
																    _lowercase      ,     kernel_size=(1, 1)      ,     padding="VALID"      ,     kernel_init=nn.initializers.zeros_init()      ,     bias_init=nn.initializers.zeros_init()      ,     dtype=self.dtype      ,     )
																controlnet_down_blocks.append(_lowercase					)

											if not is_final_block:
																__SCREAMING_SNAKE_CASE				:    List[str]						    =							nn.Conv(
																    _lowercase      ,     kernel_size=(1, 1)      ,     padding="VALID"      ,     kernel_init=nn.initializers.zeros_init()      ,     bias_init=nn.initializers.zeros_init()      ,     dtype=self.dtype      ,     )
																controlnet_down_blocks.append(_lowercase					)

						__SCREAMING_SNAKE_CASE				:    List[str]						    =							down_blocks
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							controlnet_down_blocks

						# mid
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							block_out_channels[-1]
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							FlaxUNetMidBlockaDCrossAttn(
						    in_channels=_lowercase      ,     dropout=self.dropout      ,     num_attention_heads=num_attention_heads[-1]      ,     use_linear_projection=self.use_linear_projection      ,     dtype=self.dtype      ,     )

						__SCREAMING_SNAKE_CASE				:    int						    =							nn.Conv(
						    _lowercase      ,     kernel_size=(1, 1)      ,     padding="VALID"      ,     kernel_init=nn.initializers.zeros_init()      ,     bias_init=nn.initializers.zeros_init()      ,     dtype=self.dtype      ,     )






	def __call__(      self      ,     a__      ,     a__      ,     a__      ,     a__      ,     a__ = 1.0      ,     a__ = True      ,     a__ = False      ,     ):
						__SCREAMING_SNAKE_CASE				:    Dict						    =							self.controlnet_conditioning_channel_order
						if channel_order == "bgr":
											__SCREAMING_SNAKE_CASE				:    str						    =							jnp.flip(_lowercase      ,     axis=1					)

						# 1. time
						if not isinstance(_lowercase      ,     jnp.ndarray					):
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							jnp.array([timesteps]      ,     dtype=jnp.intaa					)
						elif isinstance(_lowercase      ,     jnp.ndarray					) and len(timesteps.shape					) == 0:
											__SCREAMING_SNAKE_CASE				:    str						    =							timesteps.astype(dtype=jnp.floataa					)
											__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							jnp.expand_dims(_lowercase      ,     0					)

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							self.time_proj(_lowercase					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							self.time_embedding(_lowercase					)

						# 2. pre-process
						__SCREAMING_SNAKE_CASE				:    str						    =							jnp.transpose(_lowercase      ,     (0, 2, 3, 1)					)
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							self.conv_in(_lowercase					)

						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							jnp.transpose(_lowercase      ,     (0, 2, 3, 1)					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							self.controlnet_cond_embedding(_lowercase					)
						sample += controlnet_cond

						# 3. down
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							(sample,)
						for down_block in self.down_blocks:
											if isinstance(_lowercase      ,     _lowercase					):
																__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							down_block(_lowercase      ,     _lowercase      ,     _lowercase      ,     deterministic=not train					)
											else:
																__SCREAMING_SNAKE_CASE				:    Tuple						    =							down_block(_lowercase      ,     _lowercase      ,     deterministic=not train					)
											down_block_res_samples += res_samples

						# 4. mid
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							self.mid_block(_lowercase      ,     _lowercase      ,     _lowercase      ,     deterministic=not train					)

						# 5. contronet blocks
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							()
						for down_block_res_sample, controlnet_block in zip(_lowercase      ,     self.controlnet_down_blocks					):
											__SCREAMING_SNAKE_CASE				:    int						    =							controlnet_block(_lowercase					)
											controlnet_down_block_res_samples += (down_block_res_sample,)

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							controlnet_down_block_res_samples

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							self.controlnet_mid_block(_lowercase					)

						# 6. scaling
						__SCREAMING_SNAKE_CASE				:    str						    =							[sample * conditioning_scale for sample in down_block_res_samples]
						mid_block_res_sample *= conditioning_scale

						if not return_dict:
											return (down_block_res_samples, mid_block_res_sample)

						return FlaxControlNetOutput(
						    down_block_res_samples=_lowercase      ,     mid_block_res_sample=_lowercase					)





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      int   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						str 							= DPTConfig(embedding_type="""hybrid""")

			if "large" in checkpoint_url:
						a_						:						List[str] 							= 1_0_2_4
						a_						:						List[str] 							= 4_0_9_6
						a_						:						Tuple 							= 2_4
						a_						:						str 							= 1_6
						a_						:						List[str] 							= [5, 1_1, 1_7, 2_3]
						a_						:						str 							= [2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
						a_						:						Any 							= (1, 3_8_4, 3_8_4)

			if "nyu" or "midas" in checkpoint_url:
						a_						:						int 							= 7_6_8
						a_						:						List[str] 							= [1, 1, 1, 0.5]
						a_						:						Tuple 							= [2_5_6, 5_1_2, 7_6_8, 7_6_8]
						a_						:						Tuple 							= 1_5_0
						a_						:						Optional[Any] 							= 1_6
						a_						:						Union[str, Any] 							= (1, 3_8_4, 3_8_4)
						a_						:						Optional[int] 							= False
						a_						:						int 							= """project"""

			if "ade" in checkpoint_url:
						a_						:						Union[str, Any] 							= True
						a_						:						int 							= 7_6_8
						a_						:						Dict 							= [1, 1, 1, 0.5]
						a_						:						Optional[int] 							= 1_5_0
						a_						:						Tuple 							= 1_6
						a_						:						str 							= """huggingface/label-files"""
						a_						:						List[Any] 							= """ade20k-id2label.json"""
						a_						:						Tuple 							= json.load(open(cached_download(hf_hub_url(a__  ,	a__  ,	repo_type="""dataset"""))  ,	"""r"""))
						a_						:						Optional[Any] 							= {int(a__): v for k, v in idalabel.items()}
						a_						:						str 							= idalabel
						a_						:						Optional[Any] 							= {v: k for k, v in idalabel.items()}
						a_						:						Optional[int] 							= [1, 1_5_0, 4_8_0, 4_8_0]

			return config, expected_shape


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Any 							= ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if (
			    "pretrained.model" in name
			    and "cls_token" not in name
			    and "pos_embed" not in name
			    and "patch_embed" not in name
			):
						a_						:						Dict 							= name.replace("""pretrained.model"""  ,	"""dpt.encoder""")
			if "pretrained.model" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.model"""  ,	"""dpt.embeddings""")
			if "patch_embed" in name:
						a_						:						List[str] 							= name.replace("""patch_embed"""  ,	"""""")
			if "pos_embed" in name:
						a_						:						Tuple 							= name.replace("""pos_embed"""  ,	"""position_embeddings""")
			if "attn.proj" in name:
						a_						:						Optional[int] 							= name.replace("""attn.proj"""  ,	"""attention.output.dense""")
			if "proj" in name and "project" not in name:
						a_						:						Tuple 							= name.replace("""proj"""  ,	"""projection""")
			if "blocks" in name:
						a_						:						Any 							= name.replace("""blocks"""  ,	"""layer""")
			if "mlp.fc1" in name:
						a_						:						Tuple 							= name.replace("""mlp.fc1"""  ,	"""intermediate.dense""")
			if "mlp.fc2" in name:
						a_						:						List[str] 							= name.replace("""mlp.fc2"""  ,	"""output.dense""")
			if "norm1" in name and "backbone" not in name:
						a_						:						Dict 							= name.replace("""norm1"""  ,	"""layernorm_before""")
			if "norm2" in name and "backbone" not in name:
						a_						:						str 							= name.replace("""norm2"""  ,	"""layernorm_after""")
			if "scratch.output_conv" in name:
						a_						:						List[Any] 							= name.replace("""scratch.output_conv"""  ,	"""head""")
			if "scratch" in name:
						a_						:						Optional[int] 							= name.replace("""scratch"""  ,	"""neck""")
			if "layer1_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer1_rn"""  ,	"""convs.0""")
			if "layer2_rn" in name:
						a_						:						Any 							= name.replace("""layer2_rn"""  ,	"""convs.1""")
			if "layer3_rn" in name:
						a_						:						List[Any] 							= name.replace("""layer3_rn"""  ,	"""convs.2""")
			if "layer4_rn" in name:
						a_						:						Optional[Any] 							= name.replace("""layer4_rn"""  ,	"""convs.3""")
			if "refinenet" in name:
						a_						:						Tuple 							= int(name[len("""neck.refinenet""") : len("""neck.refinenet""") + 1])
						# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
						a_						:						List[Any] 							= name.replace(f'''refinenet{layer_idx}'''  ,	f'''fusion_stage.layers.{abs(layer_idx-4)}''')
			if "out_conv" in name:
						a_						:						List[Any] 							= name.replace("""out_conv"""  ,	"""projection""")
			if "resConfUnit1" in name:
						a_						:						List[str] 							= name.replace("""resConfUnit1"""  ,	"""residual_layer1""")
			if "resConfUnit2" in name:
						a_						:						Optional[int] 							= name.replace("""resConfUnit2"""  ,	"""residual_layer2""")
			if "conv1" in name:
						a_						:						List[str] 							= name.replace("""conv1"""  ,	"""convolution1""")
			if "conv2" in name:
						a_						:						Optional[Any] 							= name.replace("""conv2"""  ,	"""convolution2""")
			# readout blocks
			if "pretrained.act_postprocess1.0.project.0" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess1.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.0.0""")
			if "pretrained.act_postprocess2.0.project.0" in name:
						a_						:						str 							= name.replace("""pretrained.act_postprocess2.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.1.0""")
			if "pretrained.act_postprocess3.0.project.0" in name:
						a_						:						List[str] 							= name.replace("""pretrained.act_postprocess3.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.2.0""")
			if "pretrained.act_postprocess4.0.project.0" in name:
						a_						:						Any 							= name.replace("""pretrained.act_postprocess4.0.project.0"""  ,	"""neck.reassemble_stage.readout_projects.3.0""")

			# resize blocks
			if "pretrained.act_postprocess1.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.3"""  ,	"""neck.reassemble_stage.layers.0.projection""")
			if "pretrained.act_postprocess1.4" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess1.4"""  ,	"""neck.reassemble_stage.layers.0.resize""")
			if "pretrained.act_postprocess2.3" in name:
						a_						:						int 							= name.replace("""pretrained.act_postprocess2.3"""  ,	"""neck.reassemble_stage.layers.1.projection""")
			if "pretrained.act_postprocess2.4" in name:
						a_						:						List[Any] 							= name.replace("""pretrained.act_postprocess2.4"""  ,	"""neck.reassemble_stage.layers.1.resize""")
			if "pretrained.act_postprocess3.3" in name:
						a_						:						Union[str, Any] 							= name.replace("""pretrained.act_postprocess3.3"""  ,	"""neck.reassemble_stage.layers.2.projection""")
			if "pretrained.act_postprocess4.3" in name:
						a_						:						Dict 							= name.replace("""pretrained.act_postprocess4.3"""  ,	"""neck.reassemble_stage.layers.3.projection""")
			if "pretrained.act_postprocess4.4" in name:
						a_						:						Tuple 							= name.replace("""pretrained.act_postprocess4.4"""  ,	"""neck.reassemble_stage.layers.3.resize""")
			if "pretrained" in name:
						a_						:						List[Any] 							= name.replace("""pretrained"""  ,	"""dpt""")
			if "bn" in name:
						a_						:						Optional[int] 							= name.replace("""bn"""  ,	"""batch_norm""")
			if "head" in name:
						a_						:						Union[str, Any] 							= name.replace("""head"""  ,	"""head.head""")
			if "encoder.norm" in name:
						a_						:						Any 							= name.replace("""encoder.norm"""  ,	"""layernorm""")
			if "auxlayer" in name:
						a_						:						Any 							= name.replace("""auxlayer"""  ,	"""auxiliary_head.head""")
			if "backbone" in name:
						a_						:						Optional[Any] 							= name.replace("""backbone"""  ,	"""backbone.bit.encoder""")

			if ".." in name:
						a_						:						str 							= name.replace(""".."""  ,	""".""")

			if "stem.conv" in name:
						a_						:						Union[str, Any] 							= name.replace("""stem.conv"""  ,	"""bit.embedder.convolution""")
			if "blocks" in name:
						a_						:						Union[str, Any] 							= name.replace("""blocks"""  ,	"""layers""")
			if "convolution" in name and "backbone" in name:
						a_						:						Dict 							= name.replace("""convolution"""  ,	"""conv""")
			if "layer" in name and "backbone" in name:
						a_						:						Optional[int] 							= name.replace("""layer"""  ,	"""layers""")
			if "backbone.bit.encoder.bit" in name:
						a_						:						Union[str, Any] 							= name.replace("""backbone.bit.encoder.bit"""  ,	"""backbone.bit""")
			if "embedder.conv" in name:
						a_						:						List[str] 							= name.replace("""embedder.conv"""  ,	"""embedder.convolution""")
			if "backbone.bit.encoder.stem.norm" in name:
						a_						:						str 							= name.replace("""backbone.bit.encoder.stem.norm"""  ,	"""backbone.bit.embedder.norm""")
			return name


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						a_						:						Optional[int] 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''')
						a_						:						str 							= state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						Any 							= in_proj_weight[: config.hidden_size, :]
						a_						:						List[str] 							= in_proj_bias[: config.hidden_size]
						a_						:						List[str] 							= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_						:						str 							= in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						a_						:						Any 							= in_proj_weight[
						    -config.hidden_size :, :
						]
						a_						:						List[str] 							= in_proj_bias[-config.hidden_size :]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Dict 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Union[str, Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_							,					a_						:						str 							= get_dpt_config(a__)
			# load original state_dict from URL
			# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
			a_						:						str 							= torch.load(a__  ,	map_location="""cpu""")
			# remove certain keys
			remove_ignore_keys_(a__)
			# rename keys
			for key in state_dict.copy().keys():
						a_						:						List[str] 							= state_dict.pop(a__)
						a_						:						str 							= val
			# read in qkv matrices
			read_in_q_k_v(a__  ,	a__)

			# load HuggingFace model
			a_						:						Any 							= DPTForSemanticSegmentation(a__) if """ade""" in checkpoint_url else DPTForDepthEstimation(a__)
			model.load_state_dict(a__)
			model.eval()

			# Check outputs on an image
			a_						:						Any 							= 4_8_0 if """ade""" in checkpoint_url else 3_8_4
			a_						:						Any 							= DPTImageProcessor(size=a__)

			a_						:						Dict 							= prepare_img()
			a_						:						int 							= image_processor(a__  ,	return_tensors="""pt""")

			# forward pass
			a_						:						Optional[Any] 							= model(**a__).logits if """ade""" in checkpoint_url else model(**a__).predicted_depth

			if show_prediction:
						a_						:						List[str] 							= (
						    torch.nn.functional.interpolate(
						        outputs.unsqueeze(1)  ,	size=(image.size[1], image.size[0])  ,	mode="""bicubic"""  ,	align_corners=a__  ,	)
						    .squeeze()
						    .cpu()
						    .numpy()
						)

						Image.fromarray((prediction / prediction.max()) * 2_5_5).show()

			if pytorch_dump_folder_path is not None:
						Path(a__).mkdir(exist_ok=a__)
						print(f'''Saving model to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						print(f'''Saving image processor to {pytorch_dump_folder_path}''')
						image_processor.save_pretrained(a__)

			if push_to_hub:
						model.push_to_hub("""ybelkada/dpt-hybrid-midas""")
						image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""")


if __name__ == "__main__":
	__snake_case				:      str   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
	    type=str,
	    help="""URL of the original DPT checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=str,
	    required=False,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)
	parser.add_argument(
	    """--show_prediction""",
	    action="""store_true""",
	)

	__snake_case				:      str   		=			parser.parse_args()
	convert_dpt_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
	)


